The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 29, 2025

Filed:

Jun. 07, 2021
Applicant:

Logisteed, Ltd., Tokyo, JP;

Inventors:

Takeshi Tanaka, Tokyo, JP;

Shunsuke Minusa, Tokyo, JP;

Hiroyuki Kuriyama, Tokyo, JP;

Daichi Ojiro, Tokyo, JP;

Kiminori Sato, Tokyo, JP;

Assignee:

LOGISTEED, Ltd., Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 30/095 (2011.12); A61B 5/00 (2005.12); A61B 5/024 (2005.12); A61B 5/352 (2020.12); B60W 40/08 (2011.12); B60W 50/00 (2005.12); B60W 50/14 (2019.12); G06N 20/00 (2018.12);
U.S. Cl.
CPC ...
B60W 30/0956 (2012.12); A61B 5/02405 (2012.12); A61B 5/352 (2020.12); A61B 5/7257 (2012.12); B60W 40/08 (2012.12); B60W 50/0097 (2012.12); B60W 50/14 (2012.12); G06N 20/00 (2018.12); B60W 2050/146 (2012.12); B60W 2540/221 (2020.01); B60W 2556/10 (2020.01); B60W 2556/50 (2020.01);
Abstract

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.


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